What is SnowNLP?

Snow is a Python Library that uses NLP and it’s compatible with languages such as Chinese. To start, you have to do the initialization via the SnowNLP class as follow: from snownlp import SnowNLP s = SnowNLP(u’我喜欢红包’) It is recommended to prefix with u to indicate that this is Unicode string.

Is NLP and sentiment analysis same?

Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.

What is a good accuracy for sentiment analysis?

Setting a baseline sentiment accuracy rate When evaluating the sentiment (positive, negative, neutral) of a given text document, research shows that human analysts tend to agree around 80-85\% of the time.

Is SVM good for sentiment analysis?

Support vector machine (SVM) is a learning technique that performs well on sentiment classification. Non-negative linear combination of multiple kernels is an alternative, and the performance of sentiment classification can be enhanced when the suitable kernels are combined.

How do you Tokenize Chinese?

Now let’s begin to discuss these four ways of tokenization:

1. Character as a Token. Treat each (in our case, Unicode) character as one individual token.
2. Word as a Token. Do word segmentation beforehand, and treat each word as a token.
3. Something in between — Byte-Pair Encoding.
4. Something in between — Unigram Language Model.

Does sentiment analysis require NLP?

This is why we need a process that makes the computers understand the Natural Language as we humans do, and this is what we call Natural Language Processing(NLP). And, as we know Sentiment Analysis is a sub-field of NLP and with the help of machine learning techniques, it tries to identify and extract the insights.

Why is sentiment analysis so difficult?

Why Sentiment Analysis is Difficult? Sentiment analysis is a very difficult task due to sarcasm. The words or text data implied in a sarcastic sentence come with a different sense of meaning depending on the senders or situations. Sarcasm is remarking someone opposite of what you want to say.

How accurate is NLP?

With overall accuracy of around 95\%, the classifiers compare well with human indexing of study types. As 90,000 abstracts could be indexed accurately within hours, this method facilitates a highly streamlined approach to identifying relevant data for health economics and outcomes research.

How is SVM better than Naive Bayes?

The biggest difference between the models you’re building from a “features” point of view is that Naive Bayes treats them as independent, whereas SVM looks at the interactions between them to a certain degree, as long as you’re using a non-linear kernel (Gaussian, rbf, poly etc.).